To avoid data colonisation and allow for genuine empowerment, people must control the data they generate.
Around the turn of the century, companies started realising the economic value of data. Google started monetizing it, tailoring ads based on search queries. Facebook did pretty much the same thing years later, in the context of social media.
What these companies were doing was essentially using data for commerce to understand a customer’s preferences and selling her just what she wanted (sometimes even if she didn’t know what that was). Between 2000 and 2010, data was used largely for this kind of monetization.
Over the past five years, the new thing has been the use of data in Artificial Intelligence (AI). AI has been around as an idea for 40 years but the availability of data, a lot of it, changed its contours. The breakthrough was deep learning, which uses layers of neural networks to automate problem-solving.
Thanks to data, software and machines have become more intelligent.
Deep learning, combined with Big Data, is at the core of everything from image recognition to self-driving cars. AI has meant an even further increase in the value of data—it isn’t just about commerce now, but about automation and intelligence.
Data is the oil of the twenty-first century.
To look at how data can disrupt, one need look no further than the digital advertising business in the United States of America (US) and the payments business in China. In the US, Google and Facebook have a 71 per cent share of total digital advertising spending. In 2015-16, they captured 89 per cent of all incremental digital advertising.
China’s mobile payments are a staggering $5.5 trillion. The Chinese have done an amazing job of using QR codes for payments. These payments are dominated by two companies—Alipay, part of the Alibaba Group, and Tencent Holdings’ WeChat. These two companies own over 90 per cent of the payments market in China.
Interestingly, data combined with AI creates scale and speed.
Take Netflix in the US. Ten years back, Netflix was stuffing a DVD in a FedEx envelope and sending it to people. Today, it has over 100 million customers worldwide. It also has data on who is watching what, when, how, and what they like. It is using this data to help create better programming. When Netflix began, it was not in the content business but in the distribution business. It started with DVDs, and then video-streamed content it didn’t own. In 2013, it started creating its own content. Its first show was House Of Cards. This year, Netflix got 93 nominations at the Emmy awards. HBO, the grand old company of TV content, had 110.
That’s the power of data.
But where is this data coming from?
Out of 5.5 billion people in the world over the age of 14, 2.5 billion have a smartphone. By 2020, every person will have four personal digital devices. The Internet of Things will soon bring 50 billion devices online.
Smart companies have realised this. Apple, Google, GE, Siemens, Amazon, Tencent, Baidu—all are moving from products and pipes to platforms. These platforms enable products that solve problems, but they also capture and own data produced in the interaction. They also use the data produced to become better at what they do. That, in turn, attracts more customers, generating more data.
Data is its own means. It is an unlimited non-rivalrous resource. Yet, it isn’t shared freely. What began as a differentiator is now the model itself. Platforms that accumulate user data disrupt industries wield disproportionate influence and create silos. This leads to data domination.
The world is just waking up to this. India should too.
There are multiple risks from data domination: violation of privacy, data colonisation, and a winner-takes-all scenario that stifles innovation and competition. This isn’t just a technology challenge but also a policy one.
We must invert the data. It has to be owned by the user and used only with her consent. Individuals should be in control of their data. It should be used to empower the individual, not the state, or the companies.
What we need, apart from a strong data protection law, is an efficient consent process. This could take the form of data consent, Application Programming Interfaces (APIs )that allow consent collection, storage, and audits. And at any time, users have the right to pull out their data. They can choose what they want to be part of, and what they don’t.
This prevents data colonisation, yet enables and empowers AI. It tilts the privacy debate in favour of the user. And it creates real user choice at every level. Data is empowering in the hands of people. Inverting it allows freedom and choice. This is data democracy.
Given the speed at which Indians are adopting the digital life, India will go from a data-poor country to a data-rich one in three years. India has a unique digital infrastructure, a set of serendipitously developed public APIs, such as eSign, Unified Payments Interface, Bharat Interface for Money, the Goods and Services Tax Network and eKYC, developed as public goods.
It also has a robust authentication infrastructure. India is the only country in the world that can empower every resident with her own data, thanks to the technology infrastructure for inversion of data available due to Aadhaar and India Stack. What it now needed is a standard and secure consent process for users to get their own data to advance their lives and a data protection law.
Darknet, also known as dark web or darknet market, refers to the part of the internet that is not indexed or accessible through traditional search engines. It is a network of private and encrypted websites that cannot be accessed through regular web browsers and requires special software and configuration to access.
The darknet is often associated with illegal activities such as drug trafficking, weapon sales, and hacking services, although not all sites on the darknet are illegal.
Examples of darknet markets include Silk Road, AlphaBay, and Dream Market, which were all shut down by law enforcement agencies in recent years.
These marketplaces operate similarly to e-commerce websites, with vendors selling various illegal goods and services, such as drugs, counterfeit documents, and hacking tools, and buyers paying with cryptocurrency for their purchases.
Anonymity: Darknet allows users to communicate and transact with each other anonymously. Users can maintain their privacy and avoid being tracked by law enforcement agencies or other entities.
Access to Information: The darknet provides access to information and resources that may be otherwise unavailable or censored on the regular internet. This can include political or sensitive information that is not allowed to be disseminated through other channels.
Freedom of Speech: The darknet can be a platform for free speech, as users are able to express their opinions and ideas without fear of censorship or retribution.
Secure Communication: Darknet sites are encrypted, which means that communication between users is secure and cannot be intercepted by third parties.
Illegal Activities: Many darknet sites are associated with illegal activities, such as drug trafficking, weapon sales, and hacking services. Such activities can attract criminals and expose users to serious legal risks.
Scams: The darknet is a hotbed for scams, with many fake vendors and websites that aim to steal users’ personal information and cryptocurrency. The lack of regulation and oversight on the darknet means that users must be cautious when conducting transactions.
Security Risks: The use of the darknet can expose users to malware and other security risks, as many sites are not properly secured or monitored. Users may also be vulnerable to hacking or phishing attacks.
Stigma: The association of the darknet with illegal activities has created a stigma that may deter some users from using it for legitimate purposes.
AI, or artificial intelligence, refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as recognizing speech, making decisions, and understanding natural language.
Virtual assistants: Siri, Alexa, and Google Assistant are examples of virtual assistants that use natural language processing to understand and respond to users’ queries.
Recommendation systems: Companies like Netflix and Amazon use AI to recommend movies and products to their users based on their browsing and purchase history.
Efficiency: AI systems can work continuously without getting tired or making errors, which can save time and resources.
Personalization: AI can help provide personalized recommendations and experiences for users.
Automation: AI can automate repetitive and tedious tasks, freeing up time for humans to focus on more complex tasks.
Job loss: AI has the potential to automate jobs previously performed by humans, leading to job loss and economic disruption.
Bias: AI systems can be biased due to the data they are trained on, leading to unfair or discriminatory outcomes.
Safety and privacy concerns: AI systems can pose safety risks if they malfunction or are used maliciously, and can also raise privacy concerns if they collect and use personal data without consent.